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Smart Service Engineering
(2019)
Industry 4.0 has provided vast opportunities for manufacturing companies whilst simultaneously creating multiple challenges. In this new highly digitized globalized marketplace, manufacturing companies find themselves under pressure to become more service oriented and offer new innovative value offerings such as smart services. These are digital data-driven services that, generally, add value in conjunction with a physical product. However, classical methods of service engineering have not adapted sufficiently to the increasing digital components and requirements of smart services. This paper presents Smart Service Engineering as a novel service-engineering approach for industrial smart services. Smart Service Engineering draws from iterative development models and implements agile and customer-centric methods to decrease the overall development time and achieve an early market success. The paper focuses on the service development steps and presents the interaction and interconnection of different elements of smart services based on a case study research. Finally the paper illustrates the successful application of the Smart Service Engineering approach and its impact on a German medium-sized company in the textile machine industry.
Industrial Smart Services - Types of Smart Service Business Models in the Digitalized Agriculture
(2018)
Due to lack of experience of companies with digital business models, agricultural machinery manufacturers and agricultural service companies are facing a positioning problem in their ecosystem. Smart services are getting more important for these companies and they have issues to define a matching business model for their newly developed smart services. The lack of a framework for smart service business models makes it even harder for companies to successfully develop new services.
This paper contributes to a better understanding of business models for smart services and establishes a common morphological framework to define different types of business models for smart services. Six types of business models of industrial smart services were identified during the research based, which was based on a literature review and interviews with leading experts in the field of smart services. The validation of the developed types and its practical application was carried out as part of the German research project Smart-Farming-World and its four developed use cases. This paper gives a detailed description of the application of the framework on the use case nPotato.
Smart Service Engineering
(2018)
Global manufacturing companies currently face an increasingly turbulent economic environment known as the "VUCA-world" (volatility, uncertainty, complexity and ambiguity). After the transformation of many companies from product to solution providers in the last 15-20 years, the focus of many corporate change processes is on digital solutions such as data-driven services. In this context, service development is of particular relevance for industrial services. Companies develop digital strategies and try to maximize the added value for their customers, by offering, for example, smart services. They are based on smart products, which are connected to the internet, interact with their environment and gather environmental data. The collected data sets are combined with other easily accessible information and processed into so-called smart data. Based on this smart data, smart services are designed. They can be defined as individualized combinations of physical and digital services. They generate added value for providers and customers and offer context-related and demand-oriented value via digital platforms. The contribution of this paper to this research field of data-driven services is a service engineering approach for industrial smart services.
Since the 1990s, service engineering has established itself as a systematic process for the development of services. Currently existing service engineering processes are based on engineering science and business model innovation toolsets. However, the increasing digital components in service engineering reveal deficits in the direct application of the classical methods of service engineering to smart services. We suggest that the successful development and implementation of smart services requires a more agile service engineering process. Studies show that companies who develop services successfully (top-performer) act up to six times faster than those with less success (follower). They involve customers in the first running prototype of their digital service to increase customer centricity and focus their development activities on core functionalities of the service to reduce its development time and test it early with customers.
To strengthen the successful development pf data-driven services in future industrial service development projects, this paper contributes to a more agile service engineering approach. Smart service engineering combines elements of linear phase models and implements agile and customer-centric findings to decrease the overall development time by focussing on core functionalities that offer a high value for customers. The paper focuses on the service development steps and presents strategic scenarios for smart service engineering. It presents the interaction and interconnection of different elements of smart services based on a case study research. In addition to this, it illustrates the implications of a customer-centric engineering approach and possible strategic decisions based on the customer feedback. The paper focuses on the successful application of the smart service engineering approach and its impact in a German medium-size company in the textile machine industry.
Smart Services
(2018)
Die Nutzung von Informations- und Kommunikationstechnologien in Wirtschaft und Gesellschaft ist inzwischen zur Selbstverständlichkeit geworden. Deutschen Leitbranchen, wie dem Maschinen- und Anlagenbau, stehen durch die Digitalisierung jedoch noch große Umbrüche vor. Die Erfassung von Daten im laufenden Betrieb der Anlagen bietet die Chance durch die Analyse der Daten wertvolle Informationen zu gewinnen. Diese Informationen lassen sich in datenbasierten Dienstleistungen mehrwertstiftend in der Instandhaltung nutzen. In diesem Beitrag wird das Potenzial von datenbasierten Dienstleistungen in der Instandhaltung erläutert und wie dadurch neue Geschäftsmo-dellen für Unternehmen entstehen können. Der Beitrag schließt ab mit einer Beschrei-bung möglicher Einsatzfelder von datenbasierten Dienstleistungen in der Instandhal-tung am Beispiel des Unternehmens BELFOR DeHaDe GmbH.
Die digital vernetzte industrielle Produktion verspricht schnellere und effizientere Prozesse - in Entwicklung und Produktion wie auch in Service, Marketing und Vertrieb oder bei Anpassung ganzer Geschäftsmodelle. Agil zu handeln und in Echtzeit Veränderungen vorzunehmen, wird in der Industrie 4.0 zur strategischen Erfolgseigenschaft eines Unternehmens. Voraussetzung dafür ist der Aufbau einer immer breiteren Datenbasis. Ob deren Potenzial effektiv genutzt wird, hängt jedoch auch wesentlich von der Organisationsstruktur und Kultur eines Unternehmens ab.
Die vorliegende acatech STUDIE stellt ein neues Instrument vor, mit dem produzierende Unternehmen den Weg zum lernenden, agilen Unternehmen individuell gestalten können. Der acatech Industrie 4.0 Maturity Index ist als sechsstufiges Reifegradmodell aufgebaut und analysiert die in der digitalisierten Industrie benötigten unternehmerischen Fähigkeiten in den Gestaltungsfeldern Ressourcen, Informationssysteme, Kultur und Organisationsstruktur. Jede erreichte Entwicklungsstufe verspricht produzierenden Unternehmen einen konkreten Zuwachs an Nutzen. Das Modell wurde in der praktischen Anwendung in einem mittelständischen Betrieb validiert.
Digitally connected industrial production promises faster and more efficient processes - in development and production, services, marketing & sales and for adapting entire business models. Agility and the ability to make changes in real time are strategic chracteristics of successful companies in Industrie 4.0. To acquire these features, it is necessary to create a continuously expanding data base. However, a company's organisational structure and culture also play an important part in determining whether this data's potential is leveraged effectively.
This acatech STUDY describes a new tool for helping manufacturing enterprises to forge their own individual path towards becoming a learning, agile company. The acatech Industrie 4.0 Maturity Index is a six-stage maturity model that analyses the capabilities in the area of resources, information systems, culture and organisational structure that are required by companies operating in a digitalised industrial environment. The attainment of each development stage promises concrete additional benefits for manufacturing companies. The model's practical application was validated in a medium-sized company.
Industrial Smart Services: Types of Smart Service Business Models in the Digitalized Agriculture
(2019)
Due to lack of experience of companies with digital business models, agricultural machinery manufacturers and agricultural service companies are facing a positioning problem in their ecosystem. Smart services are getting more important for these companies and they have issues to define a matching business model for their newly developed smart services. The lack of a framework for smart service business models makes it even harder for companies to successfully develop new services. This paper contributes to a better understanding of business models for smart services and establishes a common morphological framework to define different types of business models for smart services. Six types of business models of industrial smart services were identified during the research based, which was based on a literature review and interviews with leading experts in the field of smart services. The validation of the developed types and its practical application was carried out as part of the German research project Smart-Farming-World and its four developed use cases. This paper gives a detailed description of the application of the framework on the use case nPotato.
Unternehmen, die ihre Prozesse durch maschinelles Lernen unterstützen wollen und hierfür auf externe Dienstleister und Produkte zurückgreifen müssen, fehlen die qualifizierten Anhaltspunkte für die Auswahl eines Machine-Learning-Anbieters.
Aus dieser Motivation heraus ist die vorliegende Marktstudie Industrial Machine Learning entstanden. Sie bietet Unternehmen die Grundlage, eine fundierte Entscheidung für oder gegen den Einsatz von Machine Learning im Unternehmen zu
treffen.
Die Darstellung von realen Usecases in der vorliegenden Marktstudie veranschaulicht die konkrete Anwendbarkeit. Insbesondere damit leistet die Studie ihren Beitrag, das Thema Maschine Learning verständlich und anschaulich darzustellen.
Die Marktstudie bietet einen umfassenden Überblick über unterschiedliche Arten von Anbietern und Lösungsmöglichkeiten.
Ein Anspruch auf Vollständigkeit wird dabei nicht erhoben und wäre für die Zielsetzung nicht angebracht.
Smart-Service-Plattformen
(2019)
Smart-Service-Plattformen können einen Lösungsbaustein darstellen, um die steigende Weltbevölkerung ressourcenschonend zu ernähren. Durch die Aggregation von Daten und kontextsensitive datenbasierte Dienstleistungen können Landwirte präzise während der gesamten landwirtschaftlichen Produktion unterstützt werden, um bei gleichbleibender Versorgungsfläche den steigenden Nahrungsmittelbedarf zu decken. Die Entwicklung und der erfolgreiche Betrieb einer Smart-Service-Plattform stellen viele Unternehmen, nicht nur in der Landwirtschaft, jedoch vor große Herausforderungen, da sich die Geschäftsmodelle und -logiken einer Plattform grundlegend von herkömmlichen Produkten unterscheiden. Um Unternehmen praxisnahe Gestaltungsempfehlungen für den Erfolg einer Smart-Service-Plattforum zu geben, wurden für diesen Beitrag insgesamt 25 bereits bestehende Plattformen aus den Bereichen Smart Farming und Smart Production sowie branchenübergreifende Plattformen mittels einer Case-Study-Research hinsichtlich ihres Geschäftsmodells und ihrer jeweiligen Erfolgskriterien untersucht. Basierend auf den Ergebnissen der unterschiedlichen Case-Studys werden insgesamt neun Gestaltungsempfehlungen für den erfolgreichen Betrieb einer Smart-Service-Plattform vorgestellt, die jeweils auf die Besonderheiten der Branche eingehen und so ein umfassendes Bild für den Erfolg einer Smart-Service-Plattform geben. [https://link.springer.com/chapter/10.1007/978-3-662-59517-6_29]
Many industrial companies face their digital transformation. In addition to an existing portfolio of products and services, new digital services are being developed to offer a portfolio of smart product service systems (Smart PSS). While the development of new digital services is rarely a problem for the companies, the organization of sales and distribution of Smart PSS in particular is a key issue. The sales of Smart PSS differs considerably from the sales of only products or services and must therefore be designed differently in order to meet customer requirements and successfully commercialize the developed Smart PSS. This paper therefore describes how the sales organization of Smart PSS should be designed successfully in various forms. The network thinking methodology is used in combination with a case study research approach to describe the connection between the offered portfolio, the customer requirements and the different elements of a sales organization. Furthermore, four different types of a sales organization for Smart PSS are described. This paper gives a recommendation for companies on a design of their sales organizations on which practical implications may be developed.